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《Proceedings of the Csee》 2003-11
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APPLYING ROUGH SET THEORY TO ESTABLISH ARTIFICIAL NEURAL NETWORKS FOR SHORT TERM LOAD FORECASTING

XIE Hong, CHENG Hao-zhong, ZHANG Guo-li, NIU Dong-xiao, YANG Jing-fei ( Dept of Electric Engineering, Shanghai Jiaotong University, Shanghai 200030, china; Dept of Computation Science & Information, North China Electric Power University, Baoding 071003, china)  
Choosing input variable and networks architecture are key processes for modeling short term load forecast by artificial neural networks, in this paper a method based on rough set theory is proposed to deal with them. In the proposed approach, the key factors that affect the load forecasting are firstly identified by rough set theory and then the input variables of forecast model can be determined. On the basis of the process mentioned aboves a set of inference rules can been obtained through reductive mining process of attributes and attribute values, then a neural networks of load forecast model is established on the rule set and BP-algorithm is adopt to optimize the networks. The method indicates that load forecast model can be established according some theoretical principles and avoiding blindness. A practical application is given at last to demonstrate the usefulness of the novel method.
【Fund】: 国家自然科学基金(50077007)~~
【CateGory Index】: TP183
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